First Monday
Relative advantages of online video platforms and television according to content, technology, and cost-related attributes by Jiyoung Cha and Sylvia M. Chan-Olmsted


Abstract
This study is designed to compare consumers’ perceptions of television and online video platforms in terms of content, technology, and cost–related attributes. It also seeks to identify the specific attributes of online video platforms that contribute to the overall relative advantage of this new video alternative. Using a national mail survey of a random sample of U.S. Internet users, this study found that Internet users perceived online video platforms to be better than television in many technology and content–related attributes. The results also suggest that video content quality, interactivity, and storage capability of online video platforms contribute to improving the overall perception of the relative advantage.

Contents

Introduction
Literature review
Method
Results
Discussion and conclusions

 

 

Introduction

Online video streaming has undergone exponential growth over the past few years. As Internet and video technologies continue to evolve, it is now common for television networks and Web proprietors to offer video content online. At the same time, more people are using the Internet to watch and share video content. A comScore report in July 2011 found that 86 percent of the total U.S. Internet audience had watched online videos (comScore, 2011). As one survey conducted during 2010 found, approximately 40 percent of American households with broadband Internet access use the Internet to watch “television programs” and “movies” (Parks Associates, 2010). With such increasing popularity, certain news articles have suggested that the rise of online video platforms can be a threat to television firms (e.g., Hopewell, 2009; Schonfeld, 2010).

One assumption driving the argument is that many consumers would migrate from television to online video platforms. Specifically, an industry report shows that consumption of advertising–supported online TV shows will be 10 times more than that of paid online videos (Hopewell, 2009). Even though we can assume that a vast number of audience members of a television network will migrate to online video platforms to watch the network’s branded videos, the migration will still presumably hurt the revenues of the television network due to the discrepancy of advertising prices between ads on television and on the Web. In addition, we cannot disregard the fact that some of the branded videos on the Web are illegally shared by individual users. The viewing of such branded videos on the Web may deprive television networks of opportunities to generate more revenues from their original aired programs. Additionally, the migration to the online platform has significant implications for the process of media planning.

Given the lingering question as to whether online video platforms are a threat to, or an opportunity for, television firms, it is critical to understand how consumers perceive the Internet as a video streaming platform — and why consumers use online video platforms. Many recent studies in the area of video consumption have focused on identifying the factors that might predict consumers’ adoptions of webcasting, Internet protocol television, digital television, and so forth. However, the majority of these studies tended to focus exclusively on the new medium. In reality, online platforms coexist with traditional media, and consumers’ use of or attitude toward traditional media may influence their decision to adopt the online alternative. The introduction of the new medium might also influence consumers’ use of traditional media. Therefore, this study does not merely focus on how consumers perceive online video platforms. Considering that the relative advantage of a system or a medium in consumers’ adoption of a new communication technology is important (Rogers, 1995), this study specifically investigates how consumers perceive the relative advantages and disadvantages of the Internet as a video streaming platform compared with television. The current study attempts to offer insights into how the television industry employs different types of distribution channels and leverages the value of online platforms. It will also provide the online video industry with managerial implications in the values, strategies, and risks of a Web–based video system.

 

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Literature review

Relative advantage

The diffusion of innovations theory is widely used to explain the adoption of a new media technology. Diffusion of innovations theory suggests that consumers experience persuasion due to perceived characteristics of an innovation through various communication channels; these perceived characteristics are important factors that affect the adoption of the innovation. Characteristics of an innovation are relative advantage, complexity, compatibility, observability, and trialability (Rogers, 1995). Empirical studies confirmed that perceived characteristics of a new media technology predict adoption of the technology; however, they also found that some of the five characteristics do not affect the adoption. Therefore, most studies focused only on some of the characteristics of the technology; they did not include all of the five characteristics in predicting adoption (Busselle, et al., 1999; Li, 2004; Lin, 1998).

Empirical studies have consistently found that relative advantage is the most reliable factor that affects technology adoption in different domains (Lin, 1998, 2001; Li, 2004). Therefore, this study attempts to delve deeper into the relative advantages of the Internet as a video streaming platform compared to television. Innovation diffusion literature emphasizes the role of relative advantage in the adoption of a new technology. Rogers (1995) defined relative advantage as “the degree to which an innovation is perceived as being better than the idea it supersedes” [1]. Rogers (1995) suggested that relative advantage relates to politics, economics, convenience, social prestige and satisfaction. Numerous studies have proposed that when the new medium is more functionally desirable or has a relatively higher advantage over the older one, consumers are more likely to choose the new medium over the older one (Heikinnen and Reese, 1986; Williams, et al., 1988; Lin, 1994). Lin (2001) suggested that relative advantage is typically discussed via the aspects of 1) superior content, 2) technological benefits, and 3) cost efficiency in investigating adoption of online services. Therefore, the present study approaches the concept of relative advantage/disadvantage from these three dimensions.

Content–related attributes

There is no doubt that superior content anchors the success of most media products. Previous studies have shown the impact of content in explaining the adoption of video–related technologies or entertainment services. When television was introduced to the market, competitive content was the primary reason why it displaced radio — albeit radio later positioned itself as a niche medium to differentiate itself from television (Lin, 2001). The capability of cable networks to offer better content variety is a plausible reason why the primetime audience of broadcast networks has decreased since the early 1980s, whereas the ratings of ad–supported basic cable networks gradually increased from 1984 to 2007 (Gorman, 2008).

Cha (2008) explored the role of content variety on the intention to use different types of movie distribution channels. The findings indicated that selectivity of movies is one of the salient factors that affected college students’ decisions to choose DVD rentals over movie theaters, the Internet, and video on demand. The importance of content variety in DVD rentals also played a role in Waterman’s study (1985), which suggested that greater product diversity is a substantial advantage of home video in competing with other media. LaRose and Atkin (1991) also found that pay cable had the relative advantage of content selectivity and quality.

The importance of content is no exception in the context of online media. In a comparison between traditional media and their online counterparts (e.g., newspapers and online news), Simon and Kadiyali (2007) concluded that online media have a substantial advantage over traditional off–line media because Web sites can hold unlimited amounts of content. While some previous studies examined the role of content from a diversity or variety perspective, others investigated the role of content on consumers’ adoption of video platforms from two aspects — content variety and overall content quality (LaRose and Atkin 1991; Cha, in press). Keeping the conceptual differences between content variety and overall content quality in mind, the current study investigates how consumers perceive both content variety and quality of online video platforms and television.

Technology–related attributes

The benefits of technological advancements are another key factor that often leads consumers to the adoption of a new system. Atkin (2002) asserted that a technology’s tangible features foster consumers’ desire in considering a new medium as a substitute for an old medium. Compared with offline media, the Internet has many unique advantages. Specifically, comparing to traditional media, online media allow people to update content on an almost continuous basis; online media offer superior search capabilities; and online media allow interaction (Simon and Kadiyali 2007). Deleersnyder, et al. (2002) pointed out easy search facilities, speed of delivery, and customization options as the advantages of delivering content through online over traditional distribution channels. Similarly, Chyi and Sylvie (2000) concluded that online newspapers are technically capable of producing interactive, multimedia content such as online forums, searchable news archives, links to related stories, frequent updates, and webcasting.

The aforementioned studies focused on the advantages of online counterparts compared with traditional media, but it also is necessary to think about the disadvantages of online media compared with traditional off–line media. Even though the intrinsic natures of online media and mobile phones are different, the disadvantages of mobile phones as a content distribution platform provide opportunities to consider other technological attributes of online media. In the context of value–added services on mobile phones, Wu and Wang (2005) studied the negative impact of the perceived disadvantage on consumers’ intention to use mobile commerce. They revealed that three of the technological attributes of mobile commerce, along with price, keep consumers from adopting mobile commerce. The technological attributes included inconvenience of mobile device, transmission quality, and transmission speed. Meanwhile, Smith (2001) identified transaction security problems, difficult navigation, and low access speed as potential deterrents of adopting mobile commerce from a technology standpoint. Additionally, Anil, et al. (2003) singled out difficulty in establishing connection, screen limitation, slow loading speed, difficulty in inputting data, no standard means of payment, security and privacy concerns as deterrents to adopting value–added services on mobile phones. They found that slow loading speed and high usage cost are the most critical impediments to adopting mobile commerce, followed by high cost of Internet–enabled handsets and difficulty in establishing connection.

Cost–related attributes

Considering the perceived advantages of a new medium with respect to content and technology–related attributes, a new medium has a much better chance of replacing an older medium if the new one also provides consumers with cost advantages. Cost has always been a driving force behind the adoption of an innovation (Reagan, 2002). Porter (1985) maintained that a crucial determinant of the substitution threat is the relative price performance of substitutes.

Prior studies found empirical evidence that supports the importance of relative cost of a medium or system in influencing consumers’ decisions to adopt the medium or system. For instance, researchers pointed out that video rentals offer the relative advantage of lower admission cost over theaters (Childers and Krugman, 1987; Lin, 1993). Focusing on different types of movie platforms, Cha (2008) found that economic benefits of the Internet and video on demand as movie platforms increased consumers’ likelihood of using these platforms. Even though a new medium may have advantages with respect to content and technological attributes, cost may overshadow these advantages in media adoption decisions — if it becomes a burden to consumers. For instance, prior studies found that high costs of service usage and handsets were the most critical deterrents to mobile commerce adoption (Smith, 2001; Anil, et al., 2003).

From a managerial perspective for both television and online video platforms, it is important to know which type of video platform has a competitive advantage over the other in terms of cost. In the context of video platforms, much attention can be paid to transaction costs. Transaction costs are incurred to begin service with a provider and/or to terminate service with a previous provider. Transaction costs consist of 1) perceived price, and 2) other non–pecuniary costs, such as time spent on search for product and mental effort in choosing (Ward and Morganosky, 2003).

Perceived price is defined as “the consumer’s perceptual representation or subjective perception of the objective price of the product” (Jacoby and Olson, 1977). With respect to the perceived price of content available online in general, there has been a tendency to believe that consumers think that everything online is free. Few video sharing or television network sites charge viewers for access to videos. The price disparity between online and off–line platforms could prompt customers to switch (Ahlers, 2006). Thus, it is plausible that some consumers who seek certain video content might prefer online video platforms to television because of their perceived price benefit from online viewing.

Search costs are another dimension of transaction cost to be taken into account regarding the choice of video platforms. Search cost determines consumer behavior and eventually market structures (Bakos, 1997). Search costs include the opportunity cost of time spent searching, as well as associated expenditures such as driving, telephone calls, computer fees, magazine subscriptions, etc. Compared with the traditional approach, the online distribution platform lowered search costs by allowing consumers to access product features easily in the context of shopping (Bakos, 1998). Yet little research has empirically explored whether consumers actually perceive online video platforms to be better than television with respect to search costs. This study empirically investigates how transaction costs — perceived price and search costs — of using online video platforms and television influence consumers’ overall perceptions of the relative advantage of each video platform.

Even though some of the content, technology, and cost–related attributes of online video platforms might be intuitively expected to be better than those of television, no studies have examined whether consumers actually perceive these attributes of online video platforms to be better than those of television. Therefore, this study empirically compares how consumers perceive online video platforms and television with respect to the content, technology, and cost–related attributes. The perceptions of content, technology, and cost–related attributes of online video platforms might also affect consumers’ perceived overall relative advantage of online video platforms over television. Thus, the current study also raises the question of how the specific attributes of online video platforms actually contribute to the overall relative advantage of online video platforms. Further, this study investigates the differences between users and non–users of online video platforms with respect to the perceived attributes and the overall relative advantage of online video platforms compared with television. Accordingly, the following research questions are addressed:

RQ1: How do consumers perceive online video platforms to be better or worse than television with respect to specific content, technology, and cost attributes? What specific content, technology, and cost attributes of online video platforms affect the overall relative advantage of online video platforms?
RQ2: Do users and non–users of online video platforms differ in how they perceive online video platforms with respect to specific content, technology, and cost attributes?
RQ3: Do users and non–users of online video platforms differ in how they perceive television with respect to specific content, technology, and cost attributes?
RQ4: Do users and non–users of online video platforms perceive online video platforms differently from television with respect to specific content, technology, and cost attributes?
RQ5: Do users and non–users of online video platforms differ in how they perceive the overall relative advantage of online video platforms?

 

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Method

Measures

     Use of online video platforms and television

To avoid confusion, this study defined a few of terms for the questionnaire participants. First, the term “video content” was defined at the beginning of the questionnaire. “Video” can mean different things. According to Dictionary.com (2012), videos refer to the visualized portion of a televised broadcast. Videos can also mean television, videocassettes and videotapes, or music videos. To avoid confusion due to these multiple definitions, this study used the term “video content” instead of the term “video.” In the questionnaire, the term “video content” was defined as any type of content that is based on the combination of audio and video. Examples of video content were also given to help respondents understand the definition. Examples included television programs, music videos, movies, and YouTube clips.

This study compares the Internet, as a video streaming platform, with television. The survey first presented the fact that the Internet and television are two of various means to watch video content. Within the given context, the widely accepted definition of online video streaming was presented in the questionnaire. For the purpose of the study, “using the Internet to watch video content” was limited to the scenario of online video streaming, which refers to viewing video content on the computer through the Internet in real time. “Using television” refers to using television to view video content. Each respondent was asked whether or not he/she uses each of the video platforms, using “yes” or “no” answer categories.

     Relative advantage

To measure the relative advantage of online video platforms compared with television, three items were adapted from the study of Chan–Olmsted and Chang (2006). The three measurement items were as follows: 1) using the Internet to watch video content is better than television; 2) using the Internet to watch video content fulfills my needs for video content consumption better than television; and, 3) using the Internet to watch video content improves my lifestyle. Specifically, respondents were asked to indicate their level of agreement with each of the statements on a seven–point Likert scale (1 = strongly disagree, 7 = strongly agree). The measure was reliable (α = .915). The Cronbach’s alpha values surpassed the criterion .70 which is recommended for applied research (Nunally, 1978). The descriptive statistics indicates that the perceived relative advantage of online video platforms was low (M = 2.497, SD = 1.575).

     Fourteen attributes of online video platforms and television

Respondents were asked to evaluate a total of 14 attributes that reflect content, technology, and cost of using each of the video platforms (the Internet and television). With respect to content, respondents were asked how they perceive a) video content variety; and, b) video content quality of online video platforms and television, respectively. The items used to measure technological attributes of online video platforms and television came from varying television– and Internet–related literature. Items regarding technological attributes included a) interactivity; b) timely updates; c) navigation; d) personalization; e) storage capabilities; f) cumbersomeness of advertisements during viewing; g) usefulness of reviews and ratings; and, h) overall reliability (Chyi and Sylvie, 2000; Smith, 2001; Viswanathan, 2005; Simon and Kadiyali, 2007). To evaluate the perceived cost of using online video platforms and television, three items focusing on perceived overall financial benefit and searching costs were used. To measure searching cost, the respondents were asked to evaluate a) time efficiency; and, b) effort efficiency in searching. The measures for searching costs were adapted from Teo and Yu (2005), Srinivasan and Ratchford (1991), and Liang and Huang (1998). The respondents were asked how they perceive each of the attributes listed above on a seven–point Likert scale (1 = strongly disagree, 7 = strongly agree).

Data collection

This study used the survey data collection method. The survey sample consisted of a total of 1,500 adults in the U.S. who use the Internet. Specifically, a mailing list of 1,500 Internet users was purchased through a leading mailing list brokerage firm. The sample for the survey was randomly selected from the list of 150 million adults nationwide who use the Internet. To increase response rates, a US$1 bill was enclosed with the questionnaire as a small token of appreciation in its first mailing. Monetary incentive is a common means to encourage more people to respond to the survey in market research (Aaker, 1997). A follow–up mailing was conducted two weeks after the initial mailing, along with a questionnaire and a business reply envelope. The analysis used a total of 388 responses. The response rate of the survey was 29.6 percent.

Participants

Males accounted for 57.0 percent of the participants; 43 percent were female. The mean age of the respondents was 52.69 (SD = 12.58). Out of 388 respondents, 47.4 percent of the participants had completed college. The data indicated that 27.1 percent hold graduate degrees, and 23.2 percent completed high school. With respect to income, 26.3 percent of the respondents said that they earn US$100,000 or more. Another 21.2 percent said that their income ranges from US$40,000 to US$59,999. The median income ranges from US$60,000 to US$79,999. Approximately 87.1 percent of the respondents were non–Hispanic Caucasians. African–Americans comprised 4.5 percent of the participants, and 2.8 percent were Asians.

 

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Results

RQ1 asked what specific content, technology, and cost–related attributes of online video platforms are perceived to be better or worse than those of television. RQ1 also attempted to determine the content, technology, and cost–related attributes of online video platforms that might affect their perceived overall relative advantage. For the first portion of the investigation, an array of one–way repeated measures Analysis of Variance (ANOVA) was performed. Perceptions of the 14 attributes of the Internet and television as video platforms, which reflect content, technology, and cost dimensions, were identified as repeated factors.

Table 1 shows the results of the repeated measures ANOVAs. The results indicate that consumers perceive online video platforms better than television in terms of effort efficiency in search (F (1,350) = 6.669, p < .05, η2 = .019), time efficiency in search (F (1,352) = 38.499, p < .001, η2 = .953), interactivity (F (1,350), = 64.606, p < .001, η2 = .056), personalization (F (1,349) = 90.893, p < .001, η2 = .207), timeliness (F = (1,350) = 20.258, p < .001, η2 = .057), usefulness of reviews and ratings (F (1,351) = 23.366, p < .001, η2 = .062), pleasure of advertisements (F (1,347) = 99.017, p < .001, η2 = .222), storage capability (F (1,343) = 19.566, p < .001, η2 = .054), and instant replay (F (1,348) = 45.468, p < .001, η2 = .116).

 

Table 1

 

It was also found that consumers perceive television to be better than online video platforms in terms of the variety of video content (F (1,352) = 8.086, p < .01, η2 = .962), quality of video content (F (1,346) = 175.047, p < .001, η2 = .336), time shift functions (F (1,348) = 32.617, p < .001, η2 = .086), and reliability (F (1,353) = 40.539, p < .001, η2 = .103). When it comes to financial benefit, there was no statistically significant difference between online video platforms and television.

The second part of RQ1 asked what specific content, technology, and cost–related attributes affect the overall perception of the relative advantage of online video platforms over television. To answer the second part of RQ1, OLS regression with a backward elimination technique was performed. Backward elimination method starts with all independent variables in the model and eliminates the ones that do not make a significant contribution to the prediction (Hair, et al., 1998). Because there is no prior study that investigated specific relationships between all of the attributes and the overall relative advantage of online video platforms, this study chose the backward elimination method. The relative advantage of online video platforms was regressed on 14 specific attributes of online video platforms. There was no multicollinearity among the variables included to the model with the variation inflation factor (VIF) from 1.072 to 3.167.

As presented in Table 2, the results of the regression revealed that three perceived attributes of online video platforms positively predict the perceived overall relative advantage of online video platforms. The specific attributes are video content quality (β = .230, p < .001), interactivity (β = .152, p < .05), and storage capability (β = .154, p < .05). Figure 1 visualizes the result.

 

Table 2: Regression for the perceived attributes of online video platforms that affect the overall relative advantage of online video platforms.
Note: *p < .05; **p < .01; ***p < .001 (two–tailed).
 BSEBeta
Quality of video content.230.061.230***
Interactivity.144.058.152*
Storage capability.153.062.154*
 
F (3,329)26.953 
R2.197 
Adjusted R2.190 

 

 

Visual depiction of the results of RQ1
 
Figure 1: Visual depiction of the results of RQ1: How the perceived specific attributes of online video platforms affect the overall relative advantage of online video platforms.

 

To address the research questions that sought to determine the differences between users and non–users of online video platforms, this study divided the survey participants into users and non–users of online video platforms. Of the 388 participants, 57.0 percent said that they use the Internet to watch video content. Another 43.0 percent said that they do not use the Internet to watch online video content.

RQ2 asked how users and non–users of online video platforms perceive online video platforms with respect to content, technology, and cost–related attributes. To investigate this research question, independent–samples t–tests were performed. Table 3 illustrates the results of the t–tests. Overall, users of online video platforms perceive the specific attributes of online video platforms more positively than do non–users across all attributes of online video platforms. The only exception is the perception of advertisements during viewing. In this case, users of online video platforms (M = 4.40, SD = 1.694) are more likely than non-users (M = 3.91, SD = 1.550) to consider online advertisements to be cumbersome (t = 2.803, df = 352, p < .001).

 

Table 3

 

RQ3 addressed how users and non–users of online video platforms perceive television in terms of content, technology, and cost–related attributes. To investigate the research question, independent samples t-tests were performed (see Table 4). The t–test results revealed that users of online video platforms have less favorable perceptions of television’s video content variety than did non-users of online video platforms (t = -3.247, df = 373, p < .001), time efficiency in search (t = -2.354, df = 372, p < .05), interactivity (t = - 2.559, df = 368, p < .05), personalization (t = -2.674, df = 367, p < .01), timeliness (t = -2.710, df = 371, p < .01), and usefulness of reviews and ratings (t = -2.345, df = 371, p < .05). There were no statistically significant differences between users and non–users of online video platforms in the rest of the television attributes.

 

Table 4

 

RQ4 asked how users and non-users of online video platforms perceive online video platforms differently from television with respect to content, technology, and cost-related attributes. To directly compare perceptions of online video platforms and television, repeated measures of ANOVA were separately performed for each group (i.e., users of online video platforms and non-users of online video platforms). Table 5 shows how users of online video platforms perceive online video platforms and television. Users of online video platforms perceive online video platforms more favorable than television in regard to video content variety (F (1,211) = 11.001, p < .01, η2 = .050), financial benefit (F (1,207) = 4.147, p < .05, η2 = .020), effort efficiency in search (F (1,209) = 26.999, p < .001, η2 = .114), time efficiency in search (F (1,211) = 11.001, p < .01, η2 = .050), interactivity (F (1,209) = 108.834, p < .001, η2 = .342), personalization (F (1, 210) = 157.063, p < .001, η2 = .428), timeliness (F (1,210) = 66.298, p < .001, η2 = .240), usefulness of reviews and ratings (F (1,211) = 59.217, p < .001, η2 = .218), time shift functions (F (1,210) = 63.767, p < .001, η2 = .233), pleasure of advertisements (F (1, 212) = 40.988, p < .001, η2 = .962), storage, and instant replay (F (1,210) = 36.887, p < .001, η2 = .233). In contrast, users of online video platforms perceived online video platforms less favorable than television in terms of quality of video content (F (1,208) = 66.575, p < .001, η2 = .242) and reliability (F (1,211) = 12.184, p < .01, η2 = .055).

 

Table 5

 

Table 6 shows how non–users of online video platforms perceive online video platforms and television. Non–users view television as a medium that offers more video content variety than online video platforms (F (1,140) = 67.705, p < .001, η2 = .326). With respect to the quality of video content, non–users of online video platforms think that television provides better quality of video content (F (1,137) = 126.040, p < .001, η2 = 479) as do the users of online video platforms. While users of online video platforms think that they can benefit from using the Internet to watch video content as far as “cost” is concerned, non–users of online video platforms believe that television is more likely to provide such a benefit than online video platforms (F (1,139) = 19.309, p < .001, η2 = .122). Both users and non–users of online video platforms agree that television is more reliable than online video platforms (F (1,141) = 32.371, p < .001, η2 = .187). With respect to cumbersomeness of advertisements, both users and non–users of online video platforms think that advertisements on television during viewing is more cumbersome than advertisements on the Internet during viewing (F (1,136) = 66.845, p < .001, η2 = .330). Non–users of online video platforms think that there are no differences between television and online video platforms with respect to effort efficiency in search, time efficiency in search, interactivity, personalization, timeliness, usefulness of reviews and ratings, time shift function, storage, and instant replay. Table 7 summarizes how users and non–users of online video platforms perceive the attributes of online video platforms and television.

 

Table 6

 

RQ5 addressed whether there are differences between users and non–users of online video platforms with respect to the overall relative advantage of online video platforms. To investigate the research question, an independent samples t–test was performed. It was found that users of online video platforms (M = 2.953, SD = 1.609) are more likely than non–users (M = 1.853, SD = 1.271) to think that online video platforms have relative advantage (t = 7.426, df = 384, p < .001).

 

Table 7: Summary of the perceived attributes of online video platforms and television.
 UsersNon–users
Better attributes of online video platforms
  • Video content variety
  • Financial benefit
  • Effort efficiency in search
  • Time efficiency in search
  • Interactivity
  • Personalization
  • Timeliness
  • Usefulness of reviews and ratings
  • Less cumbersome advertisements during viewing
  • Instant replay
  • Less cumbersome advertisements during viewing
 
Better attributes of television
  • Video content quality
  • Reliability
  • Video content variety
  • Video content quality
  • Financial benefit
  • Reliability

 

 

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Discussion and conclusions

Today’s multiplatform environment enables consumers to adopt and use multiple platforms for their media consumption. Nevertheless, most prior studies have tended to focus on a single medium or a single platform to understand consumers’ media usage patterns. Such a limited emphasis provides an incomplete audience context for media planning. Given that, this study makes three contributions to the advertising media field. First, this study clarified and defined the three key dimensions of the relative advantage of new media technologies — content, cost, and technology — and empirically tested the contribution of each dimension to overall relative advantage of a media technology. Although there are numerous studies on how relative advantage influences the adoption of a new media technology, the key dimensions of relative advantage had not been sufficiently clarified and tested.

Second, this study empirically compared and contrasted relative advantages and disadvantages of an emerging video platform (i.e., the Internet as a video streaming platform) versus a traditional video platform (i.e., television). Despite the growth of online video platforms and lingering questions about their influence on television use, there exists scant scholarly research that directly compared perceptions of these two video platforms. There do exist previous studies that examined advantages and disadvantages of a new distribution platform in the context of shopping or value–added services on mobile phones. However, most of them focused only on the new distribution platform, without comparing it to traditional distribution platforms.

Third, the present study attempted to infer the population from the sample it used, and thus employed inferential statistics. Prior studies that explored advantages of a new distribution platform tended to employ descriptive statistics to find perceived advantages or disadvantages of the new distribution platform. Therefore, their findings were limited to the samples they used. The present study is not bound by such limits.

The results of this study suggest that general Internet users perceive online video platforms to be better than television in many specific attributes — namely, effort efficiency of search, time efficiency of search, interactivity, personalization, timeliness, usefulness of reviews and ratings, less cumbersome advertisements during viewing, instant replay, storage, and reliability. These attributes represent the cost and technology dimensions of online video platforms. With respect to the content dimension, this study found that general Internet users think that television is better than online video platforms in both overall video content quality and variety. Although the Internet users generally view the aforementioned attributes of online video platforms as superior to those of television, this study also found that only three attributes of online video platforms actually influence the overall relative advantage of online video platforms. The three features are quality of video content, interactivity, and storage capability. Previous studies consistently found that the overall relative advantage of a new media technology influences intention to adopt the technology (Dwivedi, 2008; Holak and Lehmann, 1990; Lin, 1998, 2001; Li, 2004). Therefore, the present study suggests that the online video services that emphasize enhanced video content quality, interactivity, and storage capability of online video platforms may be more attractive media vehicles for advertisers.

Lin (2001) suggested that the relative advantage of a new technology or medium is conceptually reflected in three aspects: content, cost, and technology. In the context of video platforms, the current study did not find cost to be a factor that contributes to the overall relative advantage of online video platforms among general Internet users. In determining relative advantage of a new video platform, consumers tend to place more emphasis on the content and technology dimensions. Prior studies found that perceived cost of a new technology predicts attitude toward the technology and the intention to adopt it (e.g., Cheong and Park, 2005; Hung, et al., 2003; Kuo and Yen, 2009). Cost may directly affect behavioral intention to adopt online video platforms or actual use of them — rather than acting as a mediating variable that influences the overall perceived relative advantage of online video platforms.

Overall, the quality of video content is the most important attribute that contributes to improving the overall relative advantage of online video platforms over television. How consumers perceive the overall quality of content available on the video platform outweighs how they perceive the other attributes when determining the overall relative advantage of the “video” platform. The importance of content quality is consistent with prior research that explored the adoption of video platforms and mobile platforms (LaRose and Atkin, 1991; Vlachos, et al., 2003). The significant contribution of product quality to improving the perceived relative advantage can be also found in online shopping literature (Ahn, et al., 2004). This study found that general Internet users perceive the video content quality of television to be better than that of online video platforms. Likewise, both users and non-users of online video platforms are also more likely, with respect to the quality of the video content, to think that television is better than online video platforms.

Although it is true that overall consumption of online video is on the rise, it is important to differentiate between the one–time viewing of an online video and the regular use of online video platforms. This study found that even though over half of the respondents have used the Internet to view video content, over 90 percent of them also said that they still use television as a primary means to watch video content. This implies that the Internet users experience hesitation or frustration with online video platforms. Such hesitation or frustration with Internet–based platforms has also been observed in the context of online shopping (Cho, et al., 2006). The present study singles out perceived video content quality of online video platforms as a barrier to the use of online video platforms. Audiences are presumably hesitant and reluctant to try online video platforms and use them regularly because of skepticism about the overall content quality of online video platforms — not primarily because of their technological characteristics.

To reinforce the position of the Internet as a video platform, it is important for the online video industry to convert non–users of online video platforms into users, and to transform one–time users into repeat users. Along with content quality, interactivity and storage capabilities influence the perceived overall relative advantage of online video platforms. However, non–users of online video platforms do not yet think that online video platforms are better than television in terms of those aspects. The non–users of online video platforms also perceive online video platforms to be very poor in many other specific attributes. One factor that hinders the growth of online distribution platforms is that certain consumers tend to stick with the traditional platforms that they are comfortable with, rather than trying the new online platforms (Linn, 2007). Considering the importance of usage experience in enhancing the perception of relative advantage, the creation of online video “trial” opportunities might be important. While this study focuses on the relative advantage variable of video platforms, future studies can investigate how observability and trialability of online video platforms influence use of online video platforms versus television.

The only attribute of online video platforms that the non–users consider to be better than television is that advertisements during one viewing are less cumbersome. Users of online video platforms also think that the advertisements are less cumbersome on online video platforms than when seen on television. The findings indicate the possibility that advertisers may be more likely to migrate from television to online video platforms in order to increase the effectiveness of their advertisements. When the findings are combined with the fact that online video platforms enable advertisers to reach specific consumer segments more efficiently, online video operators can also promote the findings to attract more advertisers to their venues.

Using a comparative study approach in evaluating two video consumption outlets concurrently, this study provides insights on the state of consumer perceptions regarding the relative advantages of the two types of video platforms and their contributors within a multiplatform video environment. This study limited the number of attributes of video platforms to 14. Future research can use a more extensive list of relevant attributes to further investigate the various aspects of video consumption. As a starting point, this study focused on relative advantages of different video platforms and whether users and non–users of online video platforms perceive them differently. Future research can continue to identify other adoption factors that might influence consumer preference regarding the two types of video platforms. End of article

 

About the authors

Jiyoung Cha Cha is an assistant professor in the film and video studies program at George Mason University. Her research focuses on adoption of new media, business models of new media, and interrelationships between new technologies and traditional media from management and marketing perspectives. Her scholarly research has been published or is forthcoming in refereed journals including the Journal of Media Economics, International Journal on Media Management, Journalism and Mass Communication Quarterly, Telematics and Informatics, Journal of Advertising Research, and the Journal of Electronic Commerce Research, among others. She received her Ph.D. in mass communication, with a minor in marketing, from the University of Florida and her master’s degree in television, radio, and film at the S.I. Newhouse School of Communications at Syracuse University.
E–mail: jcha2 [at] gmu [dot] edu

Sylvia M. Chan–Olmsted is Associate Dean for Research — Division of Graduate Studies and Professor in the Department of Telecommunication at the University of Florida. She is the author of Competitive strategy for media firms (Erlbaum Associates, 2005), co–editor Handbook of media management and economics (Erlbaum Associates, 2006) and Global media economics (Iowa State University Press, 1998), and author of numerous book chapters and articles published in the Journal of Broadcasting & Electronic Media, Journalism & Mass Communication Quarterly, and the Journal of Media Economics. Dr. Chan–Olmsted teaches classes in audience analysis, telecommunication management, media strategy and competition, brand management, and research methods. Dr. Chan–Olmsted has received research grants from institutions such as the National Association of Broadcasters (NAB), Magness Institute at Cable Center, Arbitron, and Center for International Business Education and Research (CIBER).

 

Acknowledgements

This research was partially funded by the Madelyn Lockhart Dissertation Fellowship.

 

Note

1. Rogers, 1995, p. 212.

 

References

Douglas Ahlers, 2006. “News consumption and the new electronic media,” Harvard International Journal of Press/Politics, volume 11, number 1, pp. 29–52.http://dx.doi.org/10.1177/1081180X05284317

Tony Ahn, Seewon Ryu, and Ingoo Han, 2004. “The impact of the online and offline features on the user acceptance of Internet shopping malls,” Electronic Commerce Research and Applications, volume 3, number 4, pp. 405–420.http://dx.doi.org/10.1016/j.elerap.2004.05.001

Samtani Anil, Leow Tze Ting, Lim Hoon Moe, and Goh Po Gin Jonathan, 2003. “Overcoming barriers to the successful adoption of mobile commerce in Singapore,” International Journal of Mobile Communications, volume 1, numbers 1/2, pp. 194–231.

J. Yannis Bakos, 1998. “The emerging role of electronic marketplaces on the Internet,” Communications of the ACM, volume 41, number 8, pp 35–42.http://dx.doi.org/10.1145/280324.280330

J. Yannis Bakos, 1997. “Reducing buyer search costs: Implications for electronic marketplaces,” Management Science, volume 43, number 2, pp. 1,676–1,692.

Jiyoung Cha, in press, “Usage of video sharing Websites: Drivers and barriers,” Telematics and Informatics.

Jiyoung Cha, 2008. “Competition among sequential movie distribution channels: An exploration of audience preference of theatrical and non–theatrical movie distribution channels,” paper presented at the meeting of the Association for Education in Journalism & Mass Communication (Chicago).

Sylvia M. Chan–Olmsted and Byeng–Hee Chang, 2006. “Audience knowledge, perceptions, and factors affecting the adoption intent of terrestrial digital television,” New Media & Society, volume 8, number 5, pp. 773–780.http://dx.doi.org/10.1177/1461444806067588

Je Ho Cheong and Myeong–Cheol Park, 2005. “Mobile Internet acceptance in Korea,” Internet Research, volume 15, number 2, pp. 125–140.http://dx.doi.org/10.1108/10662240510590324

Terry L. Childers and Dean M. Krugman, 1987. “The competitive environment of pay per view,” Journal of Broadcasting & Electronic Media, volume 31, number 3, pp. 335–342.http://dx.doi.org/10.1080/08838158709386668

Chang–Hoan Cho, Jaewon Kang, and Hongsik John Cheon, 2006. “Online shopping hesitation,” CyberPsychology & Behavior, volume 9, number 3, pp. 261–274.http://dx.doi.org/10.1089/cpb.2006.9.261

Hsiang Iris Chyi and George Sylvie, 2000. “Online newspapers in the U.S.: Perceptions of markets, products, revenue, and competition,” International Journal on Media Management, volume 2, number 2, pp. 69–77.http://dx.doi.org/10.1080/14241270009389924

comScore, 2011. “comScore releases July 2011 U.S. online video rankings” (22 August), at http://www.comscore.com/Press_Events/Press_Releases/2011/8/comScore_Releases_July_2011_U.S._Online_Video_Rankings, accessed 1 December 2011.

Barbara Deleersnyder, Inge Geyskens, Katrijin Gielens, and Marnik G. Dekimpe, 2002. “How cannibalistic is the Internet channel? A study of the newspaper industry in the United Kingdom and the Netherlands,” International Journal of Research in Marketing, volume 19, number 4, pp. 337–348.http://dx.doi.org/10.1016/S0167-8116(02)00099-X

Dictionary.com, 2012. “Dictionary.com,” at http://dictionary.reference.com, accessed 10 March 2012.

Yogesh K. Dwivedi, 2008. Consumer adoption and usage of broadband. Hershey, Pa.: IRM Press.

Bill Gorman, 2008. “Where did the primetime broadcast audience go?” TV by the Numbers (16 April), at http://tvbythenumbers.com/2008/04/16/where-did-the-primetime-broadcast-audience-go/3362, accessed 5 October 2009.

Kalle J. Heikinnen and Stephen D. Reese, 1986. “Newspaper readers and a new information medium: Information need and channel orientation as predictors of videotex adoption,“ Communication Research, volume 13, number 1, pp. 19–36.http://dx.doi.org/10.1177/009365028601300103

Susan Holak and Donald Lehmann, 1990. “Purchase intentions and dimensions of innovation: An exploratory model,” Journal of Product Innovation Management, volume 7, number 1, pp. 59–73.http://dx.doi.org/10.1016/0737-6782(90)90032-A

John Hopewell, 2009. “Free online TV a threat to industry: Screen Digest unveils report at media confab,” Variety (24 March), at http://www.variety.com/article/VR1118001582, accessed 1 March 2012.

Shin–Yuan Hung, Cheng–Yuan Ku, and Chia–Ming Chang, 2003. “Critical factors of WAP services adoption: An empirical study,” Electronic Commerce Research and Applications, volume 2, number 1, pp. 46–60.http://dx.doi.org/10.1016/S1567-4223(03)00008-5

Jacob Jacoby and Jerry C. Olson, 1977. “Consumer response to price: An attitudinal information processing perspective,” In: Yoram Wind and Marshall G. Greenberg (editors). Moving a head [i.e. ahead] with attitude research: [Proceedings] of the Seventh Attitude Research Conference (Hilton Head Island, S.C., 1976). Chicago: American Marketing Association, pp. 73–86.

Ying–Feng Kuo and Shieh–Neng Yen, 2009. “Towards an understanding of the behavioral intention to use 3G mobile value–added services,” Computers in Human Behavior, volume 25, number 1, pp. 103–110.http://dx.doi.org/10.1016/j.chb.2008.07.007

Robert LaRose and David Atkin, 1991. “Attributes of movie distribution channels and consumer choice,” Journal of Media Economics, volume 4, number 1, pp. 3–17.http://dx.doi.org/10.1080/08997769109358200

Shu–Chu Sarrina Li, 2004. “Exploring the factors influencing the adoption of interactive cable television services in Taiwan,” Journal of Broadcasting & Electronic Media, volume 48, number 3, pp. 466–483.http://dx.doi.org/10.1207/s15506878jobem4803_7

Ting–Peng Liang and Jin–Shiang Huang, 1998. “An empirical study on consumer acceptance of products in electronic markets: A transaction cost model,” Decision Support Systems, volume 24, number 1, pp. 29–43.http://dx.doi.org/10.1016/S0167-9236(98)00061-X

Carolyn A. Lin, 2001. “Audience attributes, media supplementation, and likely online service adoption,” Mass Communication & Society, volume 4, number 2, pp. 19–38.http://dx.doi.org/10.1207/S15327825MCS0401_03

Carolyn A. Lin, 1998. “Exploring personal computer adoption dynamics,” Journal of Broadcasting & Electronic Media, volume 42, number 1, pp. 95–112.http://dx.doi.org/10.1080/08838159809364436

Carolyn A. Lin, 1994. “Audience fragmentation in a competitive video marketplace,” Journal of Advertising Research, volume 34, number 6, pp. 30–38.

Carolyn A. Lin, 1993. “Modeling the gratification–seeking process of television viewing,” Human Communication Research, volume 20, number 2, pp. 224–244.http://dx.doi.org/10.1111/j.1468-2958.1993.tb00322.x

Allison Linn, 2007. “Online shopping growth to slow in the next decade: Despite convenience, some shoppers don’t trust online retailing,” MSNBC (4 September), at http://www.msnbc.msn.com/id/20321999/, accessed 10 March 2012.

Parks Associates, 2010. “Consumption of PC video is increasing, but TV still main video source for households” (17 August), at http://www.marketwire.com/press-release/consumption-of-pc-video-is-increasing-but-tv-still-main-video-source-for-households-1305766.htm, accessed on 5 April 2012.

Michael E. Porter, 1985. Competitive advantage: Creating and sustaining superior performance. New York: Free Press.

Joey Reagan, 2002. “The difficult world of predicting telecommunication innovations: Factors affecting adoption,” In: Carolyn A. Lin and David J. Atkin (editors). Communication technology and society: Audience adoption and uses. Cresskill, N.J.: Hampton Press, pp. 65–87.

Everett M. Rogers, 1995. Diffusion of innovations. Fourth edition. New York: Free Press.

Erick Schonfeld, 2010. “Estimate: 800,000 U.S. households abandoned their TVs for the Web,” TechCrunch (13 April), at http://techcrunch.com/2010/04/13/800000-households-abandoned-tvs-web/, accessed 14 February 2012.

Daniel H. Simon and Vrinda Kadiyali, 2007. “The effect of a magazine’s free digital content on its print circulation: Cannibalization or complementarity?” Information Economics and Policy, volume 19, numbers 3–4, pp. 344–361.

Brad Smith, 2001. “Consumer apathy impairs m–commerce,” Wireless Week, volume 7, number 29, p. 6.

Narasimhan Srinivasan and Brian T. Ratchford, 1991. “An empirical test of a model of external search for automobiles,” Journal of Consumer Research, volume 18, number 2, pp. 233–242.http://dx.doi.org/10.1086/209255

Thompson S.H. Teo and Yuanyou Yu, 2005. “Online buying behavior: A transaction cost economics perspective,” Omega, volume 33, number 5, pp. 451–465.http://dx.doi.org/10.1016/j.omega.2004.06.002

Siva Viswanathan, 2005. “Competing across technology–differentiated channels: The impact of network externalities and switching costs,” Management Science, volume 51, number 3, pp. 483–496.http://dx.doi.org/10.1287/mnsc.1040.0338

Pavlos Vlachos, Adam Vrechopoulos, and Georgios Doukidis, 2003. “Exploring consumer attitudes towards mobile music services,” International Journal on Media Management, volume 5, number 2, pp. 138–148, and at http://www.mediajournal.org/ojs/index.php/jmm/article/view/251, accessed 19 September 2012.

Michael R. Ward and Michelle Morganosky, 2003. “Online consumer search and purchase in a multiple channel environment,” In: Michael R. Baye (editor). The economics of the Internet and E–commerce. Advances in Applied Microeconomics, volume 11. Greenwich, Conn.: JAI Press.

David Waterman, 1985. “Pre–recorded home video and the distribution of theatrical feature films,” In: Eli M. Noam (editor). Video media competition: Regulation, economics, and technology. New York: Columbia University Press, pp. 221–243.

Frederick Williams, Ronald E. Rice, and Everett M. Rogers, 1988. Research methods and the new media. New York: Free Press.

Jen–Her Wu and Shu–Ching Wang, 2005. “What drives mobile commerce? An empirical evaluation of the revised technology acceptance model,” Information and Management, volume 42, number 5, pp. 719–729.http://dx.doi.org/10.1016/j.im.2004.07.001

 

Editorial history

Received 23 April 2012; accepted 3 September 2012.

Copyright © 2012, First Monday.
Copyright © 2012, Jiyoung Cha and Sylvia M. Chan–Olmsted. All rights reserved.

Relative advantages of online video platforms and television according to content, technology, and cost–related attributes
by Jiyoung Cha and Sylvia M. Chan–Olmsted
First Monday, Volume 17, Number 10 - 1 October 2012
https://firstmonday.org/ojs/index.php/fm/article/download/4049/3328
doi:10.5210/fm.v17i10.4049